What triggers children to play volleyball? : A research into the underlying factors influencing the intention to actively play volleyball with engagement levels in marketing communication tools as a key role.

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WHAT TRIGGERS CHILDREN TO PLAY VOLLEYBALL?

A research into the underlying factors influencing the intention to actively play volleyball with engagement levels in marketing communication tools as a key

role.

Master Thesis Examination Committee

Jette Blokhuis Dr. M. Galetzka

S2412195 Drs. MH. Tempelman

University of Twente

Faculty of Behavioral, Management & Social Sciences

Master Communication Science – Digital Marketing Communication June 2021

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Abstract

Recently sport associations battle with losing memberships, especially among children, and would like to know how they can attract children toward their sport. In collaboration with the Nevobo (Dutch Volleyball Association) this research provides insights into the underlying factors influencing children to actively play volleyball, while in addition these factors are influenced by engagement levels in marketing communication tools. In order to assess the underlying factors, constructs of Theory of Planned Behavior (attitude, injunctive norm, descriptive norm, self-efficacy and peer acceptance) and Self-Determination Theory (intrinsic motivation, introjected regulation, identified regulation, and external regulation) were used in this study. In addition, four videos were created based on level of engagement, starting with inflatable volleyball equipment (volleyball bus) as low level of engagement, then a challenge, an interactive wall, and a VR game (which is considered the highest level of engagement). By means of a quasi-experiment, children aged 8 to 12 years old were asked to watch one of the movies (except for the control group) followed by a survey they had to fill out. Through self- report measures attitude, injunctive norm, descriptive norm, self-efficacy, peer acceptance, intrinsic motivation, introjected regulation, identified regulation, and external regulation were measured. Analyses were performed to assess the effect the different videos have on the psychological factors and to measure the effect of the psychological factors on active volleyball participation. The results of this study suggest that barely no effect is seen on engagement levels in marketing communication tools with the exception of peer acceptance on the volleyball bus as a significantly higher score was found in comparison to the control group. Moreover, injunctive norm, peer acceptance, intrinsic motivation, introjected regulation, and identified regulation showed a significant positive influence on active volleyball participation. These new insights could help academics to do better research among children and sport participation. In addition, the current study gives insights on which factors influence active volleyball participation and how the Nevobo can implement this in their marketing communication strategy.

Keywords

Theory of planned behavior, self-determination theory, active volleyball participation, engagement levels in marketing communication tools

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1. Introduction

The Dutch Volleyball association Nevobo is battling with losing members and it is not the only one. In the last couple of years several sports associations, including the bigger Dutch associations such as KNVB (soccer) and KNLTB (tennis), are struggling with declining memberships (AD, 2018; NOC*NSF, 2020). From 2013 to 2019 there was a decline of 220,200 members among sport associations (NOC*NSF, 2020). NOC*NSF, which is the umbrella organization for sports in the Netherlands, states that Covid-19 led to a decline of 44% in the past year in sports participation (Omroep West, 2021). This will not only result into a decline in physical health but also mental health (Omroep Weste, 2021). In addition, when taking a closer look at the decline in membership. Sladek (2013) states that the current generation attaches less value towards memberships. Where traditionally the association was the main place to practice sports, now there are many other alternatives. This is in line with the NOC*NSF (2020), which states that the Nevobo has a total of 112,000 members but 239,000 athletes. This means that not everybody who plays volleyball is a member of the sport association. Besides that, the NOC*NSF (2020) shows that the number of members did indeed decrease from 2013 till 2019 with 8% among 5 through 9 years old and 5% among 10 through 14 years old. However, the number of athletes that play volleyball increased with 3% (aged 5- 9) and 4% (aged 10-14). The same report shows that among boys (aged 5-18) soccer is by far the most popular sport (volleyball does not make it in the top 10), while for girls (aged 5-18) the number one sport in 2019 was swimming (volleyball comes in on the 10^th place).

In order to increase the amount of members at the Nevobo, it is important to understand what triggers children to actively play volleyball. According to Funk et al. (2016) every “sport interaction involves an individual’s psychological and physical responses, and includes beliefs, emotions and perceptions that occur before, during and after the use or anticipated use of a sport product or service” (p. 3). Therefore, in order to enhance sports participation and engage in sports it is important to understand why people would participate in a sport and thus understand their behavior. Previous research used the Theory of Planned Behavior (TPB, Ajzen, 1991) as a framework to understand human behaviors in relation to exercise behavior (e.g. Armitage &

Conner, 2001; Downs & Hausenblas, 2005; McEachan, Conner, Taylor, & Lawton, 2011).

According to the TPB model (Ajzen, 1991), an individual’s personal behavior is defined by one’s behavioral intention to perform the behavior. In turn, behavioral intention is defined by three factors: attitude, subjective norms, and perceived behavioral control.

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6 Downs and Hausenblas (2005) performed a meta-analysis to predict exercise related behavior and found out that TPB could explain 30.4% of the variance in intention with attitude and PBC as the largest predictors. A few years later, McEachan, Conner, Tayler, and Lawton (2011) also conducted a meta-analysis to predict exercise related behavior and were able to explain 44.3% of the variance in intention, also with attitude and PBC as the largest predictors.

When taking a closer look at children and exercise behavior, researchers claim that social norms play a more important role with children than with adolescents (Allen, 2003; Kohl & Hobbs, 1998), especially the influence of parents and peers. Research conducted by Craggs, Corder, van Sluijs, and Griffin (2011) supports this by stating that peer support has a positive association with physical activity.

In addition, another factor that plays an important role in sports participation is motivation (Dilsad, Kin Yan Ho, Al-Haramlah, & Mataruan-Dos Santos, 2020; Matsumoto & Takenaka, 2004). Research has shown that individuals with strong intentions are more likely to be motivated to perform the behavior and to expend efforts to achieve their goals (Norman, Clark,

& Walker, 2005). Meta-analyses have indicated that the TPB typically explains between 40%

and 50% of the variance in intention (e.g. Godin & Kok, 1996), and between 23% and 34% of the variance in behavior (e.g. McEachen et al., 2011). Therefore, Ajzen (1991) suggested that additional predictors could be added to TPB if they account for a considerable variance, which in this case will be motivation.

Therefore, the self-determination theory (SDT; Deci & Ryan, 1985) will be used to measure motivation as it has shown to be useful allowing us to explain the why and wherefore of people’s behavior and helps us understand the reasons why one is committed to sports (Moreno-Murica et al., 2013). SDT consists of three “fundamental forms of behavior regulation: self-determined, non-self-determined or demotivated” (Moreno-Murica et al., 2013, p. 551). Self-determined motivations tend to be intrinsically driven, while non-self-determined motivation are performed because they must be done (Moreno-Murica et al., 2013). Demotivation is characterized by a lack of motivation (Moreno-Murica et al., 2013).

Moreover, SDT has been widely used to understand one’s motivations to participate in physical activity, mainly done on adolescents subjects (Dilsad et al., 2020; Kondric, Sindik, Furjan-Madic, & Schiefler, 2013). However, Sebire, Jago, Fox, Edwards, and Thompson (2013) researched the validity and reliability of SDT for physical activity among children aged 7 to 11 years old and showed that these measures were supported among this age group. Children’s

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7 motivation is mostly based on enjoyment and inherent satisfaction of physical activity (Kohl &

Hobbs, 1998; Sebire Jago, Fox, Edwards, & Thompson, 2013). This is in line with McCullagh, Matzkanin, Shaw, and Meldonado (1993) stating that intrinsic motives such as having fun and feeling good are primary reasons for sports participation among children. Additionally, among children a form of rewards and punishments also appeared to be important (Kohl & Hobbs, 1998). A research conducted by Foley, Beets, and Cardinal (2011) showed that when children were monitored during their play their activity increased. Therefore, motivation among this age group is seen as a relevant factor and thus SDT and TPB will be combined in the current study.

Besides measuring the factors that has an impact on one’s behavior, Funk et al. (2016) states that it is possible to influence the psychological factors of SDT and TPB with marketing activities as these are considered to be the primary sources of information that helps a member to determine whether the sport will satisfy one’s needs and wants. This is in line with Sladek (2013), who states that the younger generation has different values than the older generation when it comes to membership and marketing plays a key role in this. Therefore, it is interesting to see whether certain marketing communication tools can trigger these factors. Previous research has shown that engagement is a very important aspect when it comes to children and marketing (Grover, 2019). There are several marketing tools that can help engage children better. Therefore, the level of engagement in marketing communication tools will be the key role for the current study.

Hence, several marketing communication tools based on level of engagement will be tested.

The Nevobo has a so-called volleyball bus with inflatable volleyball equipment to introduce children to volleyball while at the same time they can participate in volleyball related activities and test their skills. This bus is used to promote volleyball and can be requested by schools and clubs but is also used as a side-event during big events. Grover (2019) states that for children it is important that they enjoy themselves and have fun playing. Therefore, the volleyball bus is seen as a perfect fit. In order to increase the level of engagement, several items will be added to the volleyball bus to increase the level of engagement; a challenge, an interactive wall, and a VR game.

The current study aims to provide insight on children’s behavioral intention to actively participate in volleyball. Combing SDT and TPB could help academics to see which factors have a significant influence on the behavioral intention. New insights could help sports

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8 associations to know which psychological factors should be triggered during i.e. marketing campaigns. The following research question is created as a guidance for the current study:

‘Which psychological factors (attitude, socials norms, PBC and motivation) positively influence Dutch children’s (aged 8-12) intention to actively participate in volleyball?’

However, the current study will not only combine SDT and TPB in order to get a better understanding on children’s behavioral intention to actively participate in volleyball but will also test whether marketing communication tools can trigger the factors that influence one’s behavior. Especially, if a higher level of engagement will lead to a higher impact on the psychological factors. Therefore, the following research question is created as well:

‘To what extent does engagement levels in marketing communication tools impact the psychological factors (attitude, social norms, PBC and motivation)?’

In order to answer these questions, an empirical study has been carried out. In the current study, Dutch children were asked to participate in an online survey where they were randomly assigned to watch one video (marketing communication tool) followed by survey questions.

Based on self-reported measure the relationship between the marketing communication tool and the psychological factors as well as the relationship between the psychological factors and behavioral intention were examined.

2. Theoretical Framework

This chapter will provide the theoretical foundation of the factors that are related to active sport participation. It is important to understand what the underlying factors are that drive children to play a volleyball and thus gives insights on why children actively participate in playing volleyball. In this section, the sport consumer decision-making model, Theory of Planned Behavior (TPB) and self-determination theory (SDT) will be discussed. In addition, the four marketing communication tools, volleyball bus, challenge, interactive wall, and VR game, will be further explained. This chapter will be concluded with a conceptual research model in order to provide an overview on how the marketing communication tools and the underlying factors are expected to influence each other.

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2.1 Sport consumer decision-making model

According to Funk et al. (2016) ‘a typical consumer decision-making model includes the study of both external and internal forces” (p. 7). Figure 1 represents a view of a decision-making model of the sport consumer.

Fig. 1. Sport consumer decision-making model (Funk et al., 2016, p. 7)

- The input phase consists of the external forces, which in turn represent two categories:

marketing activities and socio-cultural factors. Funk et al. (2016) states that these are both key sources of information, which provide help in understanding whether or not a sport product or service will fulfill somebody’s wants and needs.

- The internal processing consists of several psychological forces, namely motivation, attitudes, personality and perception of constraints. These are the unobservable psychological mechanisms where the sport customer evaluates the inputs of the first stage (Funk et al., 2016).

- The output phase is the final stage of the sport consumer decision-making model. This stage relates to both behavioral and attitudinal outcomes (Funk et al., 2016).

The sport consumer decision-making model places emphasis on how decisions are made through a series of inputs, internal processing, and outputs. This model can be linked to the current study as following. The input phase are the four marketing communication tools that will be used to promote volleyball and create a higher level of engagement among the target audience. The internal processing consists of both SDT, which involves intrinsic and extrinsic motivations, and the TPB, which involves the factors attitude, subjective norms, and perceived behavioral control. The output phase will be TPB’s behavioral intention, which in this case is active participation in volleyball.

Inputs - External forces

• Marketing activities

• Socio-cultural factors

Internal processing - psychological forces

• Motivation

• Attitudes

• Personality

• Perception of constraints

Outputs

• Attitude formation and change

• Behavior and intentions

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2.2 Theory of Planned Behavior

As mentioned above the internal processing consist of psychological forces. The Theory of Planned Behavior (TPB; Azjen, 1991) is a widely acknowledge psychological framework to understand people’s behavior (e.g. Eddosary et al., 2015). According to the TPB model, an individual’s personal behavior is defined by one’s behavioral intention to perform the behavior.

In turn, behavioral intention is defined by three factors: attitude, subjective norms, and perceived behavioral control.

2.2.1 Attitude

Attitude “refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Ajzen, 1991, p. 188). Attitude is a feature of an individual's salient behavioral beliefs (Ajzen, 1991; McEachan et al., 2011), meaning the likelihood that performing a behavior will lead to certain outcomes. If one holds a positive attitude toward the belief the outcomes will be positive, if one holds a negative attitude toward the belief the outcomes will be negative. In addition, attitude is also mentioned by Funk et al.

(2016) as one of the psychological forces that predicts sports attitude formation and behavior.

In past research, attitude is found to be a powerful predictor of exercise related behavior.

Researchers found support that attitude has a great influence on and can predict behavioral intention in exercise related behavior (e.g. Downs & Hausenblas, 2005; McEachen et al., 2011).

Downs & Hausenblas (2005) and McEachen et al. (2011) both found that attitude is the strongest predictor on one’s intention related to exercise behavior. In other words, if one’s attitude toward a certain behavior is positive this will strengthen one’s intention to actually perform the behavior (Ajzen, 1991). In this case this means that if children hold a positive attitude toward active volleyball participation then it is more likely that he/she will actually actively play volleyball.

The present study defines attitude as an individual’s perception and tendencies of behavior toward actively participating in volleyball. If an individual holds a positive attitude toward active volleyball participation, then they are more likely to actively participate. Therefore, the following hypothesis is proposed:

H1: A positive attitude toward active volleyball participation is positively related to the intention to actively participate in volleyball.

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11 2.2.2 Social norms

Besides attitude, subjective norms are another factor that determines one’s behavioral intention.

In general, subjective norms have been widely used to investigate “perceived social pressure to perform or not to perform the behavior” (Ajzen, 1991, p. 188). However, past research suggested that subjective norms are very limited in predicting social influences on one’s behavioral intention (e.g. Norman, Clark, & Walker, 2005) and should be extended into two dimensions (Cialdini & Goldstein, 2004). The two dimensions together are also known as social norms and consists of a) injunctive social norms and b) descriptive social norms. Injunctive social norm is defined as one’s perception about how significant others think one should behave, while descriptive norm is defined as one’s perception about what significant others do.

Even though previous studies related to exercise behavior state that attitude is a greater predictor of behavioral intention than social norms (e.g Downs & Hausenblas, 2005; McEachen et al., 2011), several studies related to exercise behavior showed that social norms play an important role. Rhodes and Courneya (2003) showed that both injunctive and descriptive norm served as a common factor toward the prediction of the intention to exercise. When taking a closer look at children and exercise, Allen (2003) states that “youth sport participants frequently report social reasons for their involvement in sport” (p. 1) and Kohl and Hobbs (1998) even showed that peer influences are quite important with organized sports (which includes volleyball). Peer influences, in general, are of great influence in active sport participation among children (Allen, 2003; Craggs et al., 2011, Kohl & Hobbs, 1998; Wood, Taks, &

Danylchuk, 2008) and therefore, within the current study, social norms are expected to have a significant relation on active volleyball participation.

Funk et al. (2016) also mentioned the influence of one’s socio-cultural environment as an important factor in the sport-consumer decision making model and state that an individual creates several ‘reference groups’ that will have an influence on one’s decisions. While Funk et al. (2016) state that this is an input rather than a psychological mechanism, the TPB model claims social norms are considered to be a psychological factor.

In the current study, we will stick to the TPB model and refer to social norms as ‘the social influence on the intention of an individual to actively participate in volleyball’. The greater the social influence one perceives on active participation, the stronger one’s intention is to actively participate in volleyball. Based on this, the following hypotheses are proposed:

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12 H2a: Injunctive norm is positively related to the intention to actively participate in

volleyball

H2b: Descriptive norm is positively related to the intention to actively participate in volleyball.

2.2.3 Perceived Behavioral Control

The last factor of the TPB model is Perceived Behavioral Control (PBC), which “refers to the perceived ease or difficulty of performing the behavior” (Ajzen, 1991, p. 188). In other words, PBC assesses how well an individual can control aspects that might help or hinder the actions that are essential to address a certain situation. If one believes he or she has the means to engage in a certain behavior, he or she is likely to have a high level of PBC and this will lead to a positive influence on the intention to perform that behavior (Ajzen, 1991).

In the case of exercise behavior, past research claims that PBC, next to attitude, is a strong predictor of behavioral intention (e.g. Downs & Hausenblas, 2005; McEachen et al., 2011).

Several resources can influence one’s perception of control toward active sports participation.

In the case of exercise behavior among children, Downs and Hausenblas (2005) found out that self-efficacy is one of those resources. This in line with research conducted by Allen (2003) who states that among children self-confidence about their physical ability is of importance when participating in a sport.

In addition, as mentioned before, peer acceptance has a great impact on children (Allen, 2003; Craggs et al., 2011, Kohl & Hobbs, 1998; Wood et al., 2008). Peer acceptance is defined as one wants to play volleyball but as peers do not play he/she will not play either and therefore is seen as a PBC factor in the current study.

Funk et al. (2016) have mentioned perception of constraints as one of the psychological factors. Constraints have been defined as “factors that are assumed by researchers and perceived or experienced by individuals to limit the formation of leisure preferences to inhibit or prohibit participation in leisure activities” (Funk et al., 2016, p. 113). This is in line with the definition of PBC by Ajzen (1991).

In general, if one holds little control over a specific behavior because the necessary resources are not available, one’s behavioral intention will be lower even though one might have a positive attitude and/or social norm toward the specific behavior. In the current study

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13 these constraints are self-efficacy and peer acceptance. Derived from this, the third hypotheses will be:

H3a: Self-efficacy is positively related to the intention to actively participate in volleyball.

H3b: Peer acceptance is positively related to the intention to actively participate in volleyball.

2.3 Self-Determination Theory

Besides the TPB factors, Funk et al. (2016) also state that motivations are part of the internal processing phase of the sport consumer. This is supported by previous research showing that motivation is an important predictor in sports and sports participation (Dilsad et al., 2020;

Matsumoto & Takenaka, 2004). The self-determination theory (SDT; Deci & Ryan, 1985) has been widely used to understand one’s motivations to participate in physical activity (e.g. Dilsad et al., 2020; Kondric et al., 2013; Sebire et al., 2013). SDT has shown to be useful allowing us to explain the why and wherefore of people’s behavior and helps us understand the reasons why one is committed to sports (Moreno-Murica et al., 2013).

Most research on SDT has been done among adults, however, past research on exercise motivation among youth showed that motivation is a strong predictor in exercise behavior (Downs, Savage, & DiNallo, 2013; Sebire et al., 2013). SDT explains six types of motivations within two dimensions: intrinsic motivations and extrinsic motivations.

2.3.1 Intrinsic motivation

Intrinsic motivation is considered to be the most self-determined behavior. Intrinsic motivation is based on one’s “interest and satisfaction derived from being active rather than engaging for a separable outcome” (Sebire et al., 2013, p. 2). In other words, it refers to everything that pushes one from the inside.

When taking a closer look at children’s motivation in exercise behavior, research showed that it is mostly based on enjoyment and inherent satisfaction of physical activity (Kohl &

Hobbs, 1998; Sebire et al., 2013). According to McCullagh et al. (1993), intrinsic motives such as having fun and feeling good are the primary reasons for sports participation among children.

Accordingly, the following hypothesis is formulated:

H4: Intrinsic motivation is positively related to the intention the actively participate in volleyball.

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14 2.3.2 Extrinsic motivation

Extrinsic motivations are the opposite of intrinsic motivations and “refers to what drives us form the outside” (Kondric et al., 2013, p. 11). External motivations can be distinguished into four types: external, introjected, identified and integrated regulations (Deci & Ryan, 1985).

External and introjected regulations are believed to be the controlled regulatory styles while identified and integrated regulation are believed to be the autonomous regulatory styles (Lonsdale, Hodge, & Rose, 2008).

- Integrated regulations is the most self-determined form and is identified as one’s behavior that is perceived as important as one’s own needs and values. Integrated motivations share qualities with intrinsic motivation but are still considered extrinsic motivations as the goals one is trying to achieve are for reasons extrinsic to the self, instead of the intrinsic enjoyment.

- Identified regulation involves giving a conscious value toward a behavior in such a way that the action is acknowledge when it is personally important.

- Introjected regulation is described as one’s behavior that is carried out to achieve social recognition or avoid internal pressures.

- External regulation is the least self-determined form and is identified as one’s behavior that is performed because of external demands, such as rewards or constraints.

However, previous studies showed that integrated regulation does not perform well when testing among children (Sebire et al., 2013). This is because integrated regulation is an advanced form of motivation about sense of self and broader life goals, which is difficult for children to answer. Therefore, integrated regulation will not be used in the current study.

Moreover, past research showed that among children in relation to physical activity a form of rewards and punishments appeared to be important (Kohl & Hobbs, 1998), which is in line with external regulation. In addition, a study conducted by Foley et al. (2011) showed that when children were monitored during their play their activity increased. Moreover, as mentioned before, social acceptance is of high importance within this age group, which is in line with introjected regulation. Based on this, the following hypotheses are proposed:

H5a: Identified regulation is positively related to the intention the actively participate in volleyball.

H5b: Introjected regulation is positively related to the intention the actively participate in volleyball.

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15 H5c: External regulation is positively related to the intention the actively participate in volleyball

2.4 Engagement levels in Marketing Communication Tools

As mentioned before, Funk et al. (2016) argues that the input phase, which are the external factors including marketing activities, can influence the internal processing phase, which are the previously mentioned factor of TPB and SDT. Funk et al. (2016) states that marketing activities are related to the marketing mix (the P’s). It has been argued that ‘promotion’ is the key factor of the marketing mix (e.g. Mullin, Hardy, & Sutton, 2014). In addition, according to Da Silva and Las Casas (2020) promotion is a crucial and vital part of the sports experience.

According to Mullin, Hardy, and Sutton (2014) promotion is related to communication tools as the role of promotion is to inform and persuade the consumer. Therefore, in the current study, the input phase consists of marketing communication tools and is of crucial importance.

Marketing communication tools are a set of varied platforms assigned to communicate with the target audience and are great way to generate awareness among the targeted audience. In addition, marketing communication tools have been widely used to increase sport participation (e.g. Ian, 2011) and Sladek (2013) states that it plays an key role when it comes to increasing memberships.

There are several marketing communication tools that are effective in persuading one’s behavior. However, it is important to take the target audience into account when finding the right marketing communication tools. Within the current study the target audience is Dutch children aged between 8 and 12 years old, also known as tweens. Tweens has been a widely used term in marketing research and is based on being ‘in-be-tween’ childhood and teen-hood (Siegel, Coffey, & Livingston, 2004). The tween age has been defined as wide as 8 to 14 years old but researchers even argue that tweens is a state of mind rather than an age (Siegel et al., 2014). As the current study is conducted in association with the Nevobo and the preferred age group is 8 to 12 years old, this age group will be used in the current study. As mentioned before, for this age group engagement is a vital aspect within marketing (Grover, 2019). Usually engagement refers to social media behavior but in the current study it is a feature of the marketing communication technology. Therefore, the marketing communication tools were based on several levels of engagement, with the control group not receiving any level of engagement, while virtual reality is seen as the highest level of engagement.

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16 2.4.1 Volleyball bus

In order to determine which marketing communication tools would be relevant to test, within the capacity of the Nevobo, a meeting was set with the communication manager and side-events manager of the Nevobo. During this meeting several promotional tools were discussed and we came to the conclusion that the so-called volleyball bus is seen as the most relevant tool to analyze within the current study. The volleyball bus consists of inflatable volleyball equipment that are used to introduce children to volleyball while at the same time they can participate in volleyball related activities and test their skills. This bus is used to promote volleyball and can be requested by schools and clubs but is also used as a side-event during big events. Grover (2019) states that for children it is important that they enjoy themselves and have fun playing Therefore, the volleyball bus is seen as a perfect fit as children can experience what it is like to play volleyball by having fun and playing with other children. Therefore, this will be one of the marketing communication tools that will be tested and the following hypothesis is formulated:

H6a: The exposure to the volleyball bus results in higher scores on attitude, social norms PBC, and motivations as compared to no exposure to engagement elements.

2.4.2 Volleyball bus and challenge

Research conducted by Foley et al. (2011) showed that if children were monitored during their play the activity increased. Therefore, it was decided to add a challenge. Within the volleyball bus, one of the inflatables measures how hard you can hit the ball. Therefore, this inflatable is seen as a relevant tool to add a challenge in terms of ranking. The harder you hit the ball, the higher you will rank. By adding a challenge to the volleyball bus the engagement level will increase as well. Based on the reviewed literature, the following hypothesis is proposed:

H6b: The exposure to the combined effect of the volleyball bus and challenge results in higher scores on attitude, socials norms, PBC and motivations as compared to the exposure to solely the volleyball bus.

2.4.3 Interactive games

At the moment the volleyball bus only contains of the inflatables but the Nevobo wants to expand the equipment and thus the next two tools will be analyzed to see if it would be relevant to add these to the bus.

In general, research has shown that interactive games can help to stimulate sports participation. Gao et al. (2012) conducted research on the impact of an interactive dance game on children’s physical activity. They tested among 126 children aged between 9 to 11 years old

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17 and results showed that the children who participated in the interactive dance game had a positive effect on the children’s physical ability. It effectively increased the children’s physical activity participation. In addition, they also found out that it had a positive effect on the child’s self-efficacy. This is interesting for the current study as self-efficacy is a resource of PBC.

Interactive games can also help engage the children better (Grover, 2019). Therefore, within the current study, an interactive wall where children can play interactive games related to volleyball will be tested and the following hypothesis is proposed:

H6c: The exposure to the combined effect of the volleyball bus and interactive wall results in higher scores on attitude, socials norms, PBC and motivations as compared to the exposure to the combined effect of the volleyball bus and challenge.

2.4.4 Virtual Reality

With the highly interactive, physical-virtual connections a new customer experience arose and the arrival of Virtual Reality (VR) technology is forming new environments where physical and virtual objects are integrated at various levels (Flavian, Ibáñez Sánchez, & Orús, 2018). One of those levels includes the usage of VR interventions on physical activity (e.g. Kivelä, Alavesa, Visuri, & Ojala, 2019; Ng, Ma, Ho, Ip, & Fu, 2019). Ng et al. (2019) research included 1,184 healthy participants to investigate the effectiveness of exercise-based VR training. It was concluded that there was a large effect size of the VR intervention on one’s physical activity level. In addition, Kivelä et al. (2019) tested a VR game on 17 people to see whether the game encourage them to exercise. Participants said that playing the game was fun and sometimes challenging and above average stated that the VR game encouraged them to exercise. As virtual reality is highly interactive, this is considered to have the highest level of engagement with the current study. Therefore, a VR volleyball game will be tested as well and the following hypothesis is proposed:

H6d: The exposure to the combined effect of the volleyball bus and VR volleyball game results in higher scores on attitude, socials norms, PBC, and motivations as compared to the exposure to the combined effect of the volleyball bus and interactive wall.

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2.5 Behavioral intention

Within this study, the final stage will be behavioral intention. According to Ajzen (1991)

“intentions are indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior” (p. 181). In other words, the stronger one’s intention is toward a certain behavior, the more likely one will perform this behavior. In the case of the relationship intention-behavior on exercise behavior, the significance of the effects between this relationship is large (e.g. Downs & Hausenblas, 2005). The previous factors mentioned above (attitude, injunctive norm, descriptive norm, self-efficacy, peer acceptance, intrinsic motivation, introjected regulation, identified regulation, and external regulation) are predictors of the intention to actively participate in volleyball. Therefore, within the current study, behavioral intention to actively participate in volleyball will be the final stage.

2.6 Conceptual research model

Based on the reviewed literature and proposed hypotheses, the following research model has been developed:

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19 Fig. 2. Conceptual research model

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3. Research Methodology

In this chapter the research design will be described in detail. First, further insights on the research design itself will be given, followed by the procedure, the research participants and measures.

3.1 Research Design

For the current study, a quasi-experimental design is used to examine the effects the psychological factors (attitude, injunctive norm, descriptive norm, self-efficacy, peer acceptance, intrinsic motivation, introjected regulation, identified regulation, and external regulation) have on active volleyball participation and the influence of engagement level in marketing communication tools on the psychological factors.

Within the current study, a quantitative approach allows to explore differences between the four different videos (volleyball bus, volleyball bus + challenge, volleyball bus + interactive wall, and volleyball bus + VR game) in order to check whether a high level of engagement has more effect on the psychological factors than no or lower level of engagement. In addition, it allows to detect a relationship between the psychological factors and the dependent variable (active volleyball participation).

First of all, it was desired to let the children use the volleyball bus in real life to really experience what it is like to play volleyball. Unfortunately, due to Covid-19 it was not possible to do this as elementary schools have been closed for quite some time and when they reopen they will not allow outside visitors. However, ideally visiting elementary schools would have been preferred, because it would allow the children to actually use the inflatables and games the volleyball bus has to offer. As this was not possible an alternative had to be chosen. As the government also advised to work from home and only travel when needed, it was decided to conduct research online through a web-based survey.

In addition, an online survey reduces the change of social desirability bias as no other respondents nor the researcher itself is present (Saunders et al., 2009). As the target audience is highly influenced by others (e.g. Allen, 2003) and it is of importance that the respondent answers the questions truthfully and is not influenced by anyone else, an online survey was seen as a good alternative.

The online survey will be set up as an experiment consisting of five different groups.

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21 - Control group will not receive any marketing communication tool but will only answer questions regarding the psychological factors and intention. By adding a control group that is not influenced by any level of engagement the effect of each marketing communication tool can be analyzed.

- Group 1 will watch a video of the volleyball bus first, then this will be followed by the same questionnaire the control group received.

- Group 2 will also watch the video of the volleyball bus first followed by a challenge and then the group will receive the questionnaire.

- Group 3 will watch a video of the volleyball bus including an interactive wall with interactive volleyball games first, then this will be followed by the questionnaire.

- Group 4 will watch a video of the volleyball bus including a VR volleyball game first, then this will be followed by the questionnaire.

3.2 Procedure

First, the four different videos to express the different kinds of engagement levels were created.

The videos were tested among peers of the author herself to indicate whether the videos indeed show different engagement levels. During the pre-test it turned out that the VR game was indeed seen as the highest level of engagement and the volleyball bus as the lowest level of engagement.

After the first version of the survey was created, a pre-test took place with one elementary school teacher, one former elementary school teacher and one person who works at a daycare.

All these people are specialist in the field of working with children and therefore they were considered to be a reliable source to test the survey. They checked if the language was suitable for children aged between 8 to 12 years old and gave general feedback concerning the survey.

All three were very helpful, especially in translating the survey to the language of the children.

For example, “What is your gender?” was changed to “Are you a boy or girl?” or to measure attitude an additional question was added namely “Volleyball seems cool to me.”. This type of language is used by children, which is known by the three specialist and therefore the pre-test was considered to be successful. After the feedback was received and the survey was amended, the survey was uploaded with the online tool Qualtrics. The final survey can be found in Appendix A.

First, the distribution of the survey took place through colleagues at the Nevobo, an elementary school teacher and a senior elementary school advisor. However, it was seen that

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22 due to Covid-19 many schools were busy with catching up on work and focusing on making it through the year. Therefore, it was decided to also distribute the survey through personal social media accounts (Facebook and Instagram) and through the researcher’s LinkedIn page.

First, all participants had to read an informed consent, which informed the participant about the purpose of the study and their rights and it also mentioned that this research was approved by the BMS ethics committee. Furthermore, it was mentioned that participating in this study is completely voluntarily and anonymous, no personal information will be recorded nor distributed. Also, it was stated that the survey was asking about one’s opinion and is not a test.

In addition, the participant was always allowed to refuse to participate in this study or withdraw from the survey at any time. As the target audience is underaged it was of utmost important that the parents/caretakers were informed about the research as well. Therefore, an informed consent was sent to the parents explaining the research and assure them of the confidentiality and anonymity of the research (see Appendix B). After the informed consent, the survey started with a few demographic questions. Next, the participant had to watch a video (except for the control group, they started with the survey immediately). After watching the video, the participant had to give his/her opinion about several items on a five-point pictorial Likert scale.

Lastly, each participant was thanked for their participation.

3.3 Research participants

Children aged between the age of 8 and 12 years old were chosen as the target audience. In addition, it was important that the participant is Dutch but besides that there were no other restrictions.

In total 306 children participated in the current study. However, 251 respondents were used for further analysis as 53 respondents did not complete the survey and two participants did not agree with the informed consent. The mean age of the participants is 10 (SD = 1.23). About half of the participants were girl (N = 134, 53.4% girls) and 46.6% were boys (N = 117). Most of the participants live in the Northern part of the Netherlands (N = 183, 72.9%), followed by Central (N = 57, 22.7%) and South (N = 11, 4.4%). In addition, most of the participants like to play sports (N = 236, 94%), some were not sure (N = 12, 4.8%) and only a small percentage dislikes playing sports (N = 3, 1.2%). Soccer is the most played sport (N = 63, 25.1%), followed by gymnastics (N = 25, 10%), dancing (N = 18, 7.2%) and volleyball (N = 15, 6%). When looking at the different groups, the control group has the most respondents (N = 75), volleyball

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23 bus (N = 48), challenge (N = 45), interactive wall (N = 38), and VR game (N = 45). An overview of the complete demographics and the differences between the groups can be found in Table 1.

Table 1

Sample’s characteristics

Control Group (N=75)

Volleyball bus (N=48)

Challenge (N=45)

Interactive wall (N=38)

VR game (N=45)

Total (N=251)

Characteristic M SD M SD M SD M SD M SD M SD

Age 10.19 1.281 9.85 1.185 10.02 1.138 10.18 1.291 10.18 1.211 10.09 1.225

N % N % N % N % N % N %

Age

8 12 16.0 7 14.6 6 13.3 5 13.2 6 13.3 36 14.3

9 8 10.7 12 25.0 7 15.6 6 15.8 6 13.3 39 15.5

10 20 36.7 14 29.2 15 33.3 11 28.9 12 26.7 72 28.7

11 24 32.0 11 22.9 14 31.1 9 23.7 16 35.6 74 29.5

12 11 14.7 4 8.3 3 6.7 7 18.4 5 11.1 30 12.0

Gender

Boy 31 41.3 16 33.3 22 48.9 22 57.9 26 57.8 117 46.6

Girl 44 58.7 32 66.7 23 51.1 16 42.1 19 42.2 134 53.4

Group

5 20 26.7 17 35.4 7 15.6 7 18.4 7 15.6 58 23.1

6 21 28.0 5 10.4 9 20.0 9 23.7 8 17.8 52 20.7

7 22 29.3 18 37.5 20 44.4 11 28.9 19 42.2 90 35.9

8 12 16.0 8 16.7 9 20.0 11 28.9 11 24.4 51 20.3

Living

North 75 100 28 58.3 25 55.6 26 68.4 29 64.4 183 72.9

Central - - 17 35.4 16 35.6 11 28.9 13 28.9 57 22.7

South - - 3 6.3 4 8.9 1 2.6 3 6.7 11 4.4

Likes playing sports

Strongly disagree - - - - - - - - - - - -

Disagree 2 2.7 - - 1 2.2 - - - - 3 1.2

Not sure 3 4.0 2 4.2 1 2.2 5 13.2 1 2.2 12 4.8

Agree 19 25.3 11 22.9 11 24.4 9 23.7 17 37.8 67 26.7

Strongly agree 51 68.0 35 72.9 32 71.1 24 63.2 27 60.0 169 67.3

Sports played

Soccer 17 22.7 10 20.8 10 22.2 9 23.7 17 37.8 63 25.1

Gymnastics 11 14.7 6 12.5 5 11.1 2 5.3 1 2.2 25 10.0

Dancing 9 12.0 4 8.3 1 2.2 4 10.5 - - 18 7.2

Volleyball 4 5.3 4 8.3 2 4.4 2 5.3 3 6.7 15 6.0

No sport 5 6.7 2 4.2 1 2.2 3 7.9 3 6.7 14 5.6

Hockey 10 13.3 - - - - 1 2.6 2 4.4 13 5.2

Tennis 1 1.3 6 12.5 3 6.7 1 2..6 2 4.4 13 5.2

Horseback riding 5 6.7 3 6.3 - - 2 5.3 - - 10 4.0

Swimming 4 5.3 - - 1 2.2 2 5.3 1 2.2 8 3.2

Basketball 4 5.3 1 2.1 1 2.2 1 2.6 - - 7 2.8

Other… 5 6.7 12 25.0 21 46.7 11 28.9 16 35.6 65 25.9

(i.e. taekwondo, kickboxing, judo)

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24

3.4 Measures

The current study used self-reported measures through an online survey based on pictorial Likert scale. Past research employing TPB and SDT has used a five-point (e.g. Ajzen, 1991) or seven-point (e.g. Martin & McCaughtry, 2008) Likert scale, which are numbered and generally labeled. However, it is important to take the target audience into account, i.e. their ability, comprehension level, and style (Davison, McLaughlin, & Giles, 2016). Therefore, for the current study, it was considered that a full completion of the questionnaire would be stimulated by using a pictorial rating Likert scale (Davison et al., 2016; Hall, Hume, & Tazzyman, 2016;

Mellor & Moore, 2014). Pictorial Likert scales have been used on various aspects of children’s beliefs, attitudes, and feelings with little criticism toward their validity (Mellor & Moore, 2014).

The pictorial rating scale used for the current study can be found in Figure 3.

Fig. 3. Pictorial rating scale

The survey started with background demographic variables such as ‘age’ and ‘gender’.

This is important for the research to get a picture of the research sample. The remaining questions were about each construct representing the conceptual model, which will be explained further in the next part.

3.4.1 Attitude

The construct of attitude was measured through three items, which were created based on Martin, Oliver, and McCaughtry (2007), including “Volleyball seems fun to me”. The reliability of this construct was high with a Cronbach’s alpha of .92. An overview of all items used for each construct including the Cronbach’s alpha can be found in Table 2.

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25 3.4.2 Injunctive norms

Injunctive norms were measured with four items, which were derived from Hashim, Jawis, Wahat, and Grove (2014), including “I think my friends think I should try to play volleyball”.

The reliability of this construct was good with a Cronbach’s alpha of .88.

3.4.3 Descriptive norms

As with injunctive norms, descriptive norms were also measured with four items obtained from Hashim et al. (2014), including “I think my friends are enthusiastic about volleyball and if my friends are enthusiastic then so am I”. Descriptive norms also shown a high level of reliability with a Cronbach’s alpha of .92.

3.4.4 Self-efficacy

Self-efficacy is the first construct of perceived behavioral control and is measured with two items, which were derived from Hay (1992) and Chase (2001), including “I think I can play volleyball”. The reliability of the construct is good with a Cronbach’s alpha of .78.

3.4.5 Peer acceptance

Peer acceptance is the second construct of perceived behavioral control and is measured with two items as well, which were obtained from Gao et al. (2012), including “I would like to play volleyball even if my friends won’t join me”. Peer acceptance shows a high level of reliability with a Cronbach’s alpha of 0.93.

3.4.6 Intrinsic motivation

Intrinsic motivation is the first construct of motivation and is measured with two items, which were derived from Sebire et al. (2013), including “I would like to play volleyball”. The reliability of this construct is good with a Cronbach’s alpha of .89.

3.4.7 Identified motivation

As with intrinsic, identified motivation is also measured with two items derived from Sebire et al. (2013), including “I believe playing volleyball is important”. The Cronbach alpha on this construct is .81, which shows this measure is reliable.

3.4.8 Introjected motivation

As the same with the other motivations, introjected is measured with two items obtained from Sebire et al. (2013), including “I believe I should play volleyball”. The reliability of this construct is acceptable with a Cronbach’s alpha of .78.

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26 3.4.9 External motivation

External motivation, unlike the others, was measured with three items, which were obtained from McCullagh et al. (1993) and Sebire et al. (2013). The Cronbach’s alpha with all three items was relatively low (α = .61). Hence only two items (“I think that other people would want me to play volleyball” and “I think that if I don’t play volleyball other people would not be happy with me”) will be used for further analysis, with a Cronbach’s alpha of .67.

3.4.10 Intention

Intention was measured with four items obtained from Hashim et al. (2014), including “I want to play volleyball”. The reliability of this construct was high with a Cronbach’s alpha of .90.

Table 2

Items per construct

Constructs Cronbach’s α Source

Attitude α = .92 Modified from Martin, Oliver,

and McCaughtry (2007) Volleyball seems fun to me.

Volleyball seems cool to me.

Volleyball seems interesting to me.

Injunctive norm α = .88 Modified from Hashim,

Jawis, Wahat, and Grove (2014)

I think that my friends think I should try to play volleyball.

I think that my parents think that I should try to play volleyball.

I think that my classmates think that I should try to play volleyball.

I think that my teacher thinks that I should try to play volleyball.

Descriptive norm α = .92 Modified from Hashim et al.

(2014) I think my parents are enthusiastic about volleyball and

if my parents are enthusiastic then so am I.

I think my friends are enthusiastic about volleyball and if my friends are enthusiastic then so am I.

I think my classmates are enthusiastic about volleyball and if my classmates are enthusiastic then so am I.

I think my teacher is enthusiastic about volleyball and if my teacher is enthusiastic then so am I.

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27 Table 2 Continued

Constructs Cronbach’s α Source

Self-efficacy α = .78 Modified from Hay (1992)

and Chase (2001) I think I can also play volleyball.

I think I would want to try to play volleyball even though I am not sure if I am good at it.

Peer acceptance α = .93 Modified from Gao, Huang,

Liu, and Xiong (2012) I would like to play volleyball even if my friends won’t

join me.

I would like to play volleyball even if my classmates won’t join me.

Intrinsic motivation α = .89 Modified from Sebire, Jago,

Fox, Edwards, and Thompson (2013)

I think volleyball is fun.

I would like to play volleyball.

Identified motivation α = .81 Modified from Sebire et al.

(2013) I think that volleyball is important.

I see the benefits of playing volleyball.

Introjected motivation α = .78 Modified from Sebire et al.

(2013) I think that I should start playing volleyball.

I want to show other people how good I am.

External motivation α = .67 Modified from Sebire et al.

(2013) I think that other people would want me to play

volleyball.

I think that if I don’t play volleyball other people would not be happy with me.

Modified from Hashim et al.

(2014)

Intention α = .90

I would like to play volleyball with my friends on the schoolyard.

I would like to try and play volleyball sometime.

I would sign up at a volleyball club.

I want to play volleyball.

4. Results

In this chapter the results of the research study will be described in detail. Therefore, the conceptual model will be tested and the different marketing communication tools will be compared.

What triggers children to play volleyball? : A research into the underlying factors influencing the intention to actively play volleyball with engagement levels in marketing communication tools as a key role.